An adaptive-scale robust estimator for motion estimation

Although RANSAC is the most widely used robust estimator in computer vision, it has certain limitations making it ineffective in some situations, such as the motion estimation problem, in which uncertainty on the image features changes according to the capturing conditions. The greatest problem is t...

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Bibliographic Details
Main Authors: Trung Ngo Thanh, Nagahara, H., Sagawa, R., Mukaigawa, Y., Yachida, M., Yagi, Y.
Format: Conference Proceeding
Language:English
Subjects:
Bandwidth
Cameras
Computer vision
Electric breakdown
Kernel
Least squares approximation
Motion estimation
Robotics and automation
Robustness
Uncertainty
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Summary:Although RANSAC is the most widely used robust estimator in computer vision, it has certain limitations making it ineffective in some situations, such as the motion estimation problem, in which uncertainty on the image features changes according to the capturing conditions. The greatest problem is that the threshold used by RANSAC to detect inliers cannot be changed adaptively; instead it is fixed by the user. An adaptive scale algorithm must therefore be applied in such cases. In this paper, we propose a new adaptive scale robust estimator that adaptively finds the best solution with the best scale to fit the inliers, without the need for predefined information. Our new adaptive scale estimator matches the residual probability density from an estimate and the standard Gaussian probability density function to find the best inlier scale. Our algorithm is evaluated in several motion estimation experiments under varying conditions and the results are compared with several of the latest adaptive-scale robust estimators.
ISSN:1050-4729
2577-087X
DOI:10.1109/ROBOT.2009.5152445